Traditionally, NRL has amounted to manually describing summaries of networks with a collection of user-selected network properties, like structural importance or subgraph counts [1]}, [2]}. Manually selected network properties provide interpretable features of a network, and have been well-utilized in applications of network science (e.g., [3]}). In the past few years, an expansive and growing suite of NRL methods have been introduced for static networks, and they have shown great promise for the exploration and analysis of complex network data – see [4]} and [5]} for recent reviews of network representation learning techniques.
